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Record W4390284116 · doi:10.23977/jeis.2023.080607

TSN Time Synchronization Based on Kalman Filtering

2023· article· en· W4390284116 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Electronics and Information Science · 2023
Typearticle
Languageen
FieldComputer Science
TopicNetwork Time Synchronization Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceEthernetTime synchronizationSynchronization (alternating current)Real-time computingKalman filterAutomotive industryProtocol (science)Protocol stackClock synchronizationEmbedded systemComputer hardwareEngineeringWireless sensor networkComputer network

Abstract

fetched live from OpenAlex

With the continuous development of the automotive industry and network technology, automotive Ethernet is facing the need for real-time and high-precision synchronization of data transmission. In this paper, the IEEE 802.1AS protocol gPTP based on the precise time protocol is studied and analysed in depth, focusing on the design and functional implementation of the time synchronization subsystem in the time-sensitive network system, and a method to improve the accuracy of time synchronization between nodes in the AUTOSAR protocol stack based on Kalman filter is given, and is implemented on the hardware platform AURIX TC397 and NXP SJA1110. Finally, the Kalman filter algorithm is used to correct the time synchronization error. Experimental results show that the proposed algorithm can significantly reduce the time deviation between the master-slave clocks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.007
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.219
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it